Questions tagged [machine-learning]
For questions related to machine learning (ML), a type of algorithm that attempts to "learn" how to perform a task without being given an explicit set of rules to follow in order to perform it. Questions on this site relate to the optimization algorithms that underpin ML, applications of ML in practical settings, and other ways that ML can be used as a tool for OR, and vice versa.
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Optimization with two constraints using Lagrange multipliers
As a part of an problem where i deploy the EM-algorithm i got stuck with the m-step that can be summarized into the below problem:
Consider the following function: $$f(\alpha_{k,l}, \theta_{n, m}) = \...
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An efficient way to find a postdoc for an operations researcher
Disclaimer: I am not sure if this is the right forum to ask this question.
I am looking for a postdoc position in operations research in the US. So far, I have found three ways:
INFORMS community (or ...
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Training ML models to be used as objectives in optimization problems
Suppose that we have data (in my case, from a chemical process) which includes input data $X$ (characteristic of the material to be processed) and decision data $Y$ (decisions taken by operators to ...
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Logic for Re-Labeling Nodes in a Directed Acyclic Graph
We are currently working at the intersection of metaheuristics and machine learning.
As part of the scheduling problem that we are trying to solve, we have a project network (directed acylic graph) ...
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What can traditional graph cut methods do well, that deep learning cannot?
I have been fascinated by the rise and fall of graph cut algorithms in recent years, which I described in this question: Was there something specific that caused graph cuts to lose popularity in the ...
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Quality of Solutions from Saddle Points vs. Local Minimums
Can Saddle Points Provide "Better Solutions" to Machine Learning Models Compared to Local Minimums?
The solution to a Machine Learning model (i.e. the final model parameters) are selected by ...
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Why does the design of heuristics require considerable domain knowledge?
I am from a machine learning (ML) background and am interested in how ML is applied to Combinatorial Optimisation. As such, as I have been reading around the area and have come across the statement ...
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Using Neural Networks For Solving Optimization Problems
Recently, I came across the below paper and found it very interesting.
Solving Mixed Integer Programs Using Neural Networks; https://arxiv.org/abs/2012.13349
The idea is to use (train with neural ...
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Interplay of OR and Statistics Research
I saw some posts like this so I figured I would start my own.
What are some interesting papers in OR that are related to, or even develop, the theory of statistical inference? What are some of the ...
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Deep Reinforcement Learning for General Purpose Optimization
Recently, I attended a very nice talk given by someone at the place I work about applying Deep Reinforcement Learning (DRL) for a design optimization problem. It was particularly interesting to me ...
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Learning local search operator selection
I'm just reading [1]. The authors use a neural network to solve capacitated vehicle routing problems through iterative generation of tours by solving a price-collecting traveling salesman problem in ...
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Which method to use to solve this multi-objective conflicting objectives
I have the following multiobjective problem.
I need to minimize the user-perceived latency while doing so aggressively minimizing user-perceived latency generates large switching cost (Reconfiguration ...
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Optimizing MIP Parameters For Various Data Sets
I have a MIP that runs for several different data sets. For each data set the MIP runs multiple times, once for each time period in the data set, and each time period is independent. I've experimented ...
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Quantifying Feasibility
I have a scheduling model formulation ( experimental setup) that takes in product states as input (sample points) and checks the model status (response) and returns feasible or infeasible.
My plan is ...
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How would you characterize "optimization data?"
We often hear that in practice, not enough data of sufficient quality, consistency, recency, etc. is available for feeding into mathematical optimization models. Example: my university wanted to plan/...
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Combining Machine learning and Operations research on a scheduling problem
I am wondering if there is any attempts of combining OR and ML in the following way. Priority-based rules are widely used in Resource Constrained Project Scheduling Problems.
Is there a way to train ...
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Interpretability Vs Accuracy in Operations Research and Management Science Community
This question might be somewhat general and not completely relevant to this forum but I think here is the most relevant place to ask the question.
Currently, deep learning, RL and generally black-...
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Best ways to use machine learning / AI as an OR scientist
I have come across GUROBI's webinar "Mathematical optimization and machine learning".
In essence, Mathematical Optimization (MO) and Machine Learning (ML) are different but complementary ...
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Queuing Theory with Learning Perspective
I am willing to work on queuing models but in classical queuing models, it is assumed the probability distributions of arrival and service are known or at least the rate is known. However, I am ...
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Which ML algorithms work by solving constrained optimization problems?
As far as I know, most machine learning algorithms solve unconstrained optimization problems, i.e., if we were to unroll all the neurons into symbolic expressions we would end up with a massive ...
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Are there any real-world problems where quadratization helps to solve something that couldn't have been solved without quadratization?
The closest thing I know is the computer vision problem, in which an image is de-blurred and/or de-noised by quadratizing a quartic problem into a quadratic optimization problem (QUBO) and then the ...
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Decoding a Deep Neural Network as an Analytical Expression for Optimization Purpose
This post is not really about a specific question but rather a topic I am curious about to know more.
We know that when it comes to integrate machine/statistical learning with optimization for the ...
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Estimation of the size of Branch-and-Bound trees using ML
A short background:
A paper [1] published in 2006 intends to show that the time needed to solve mixed-integer programming problems by branch and bound can be roughly predicted early in the solution ...
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AI gets a lot of attention these days. Does constraint optimization get more attention, too? Why (not)?
Looking at the news as well as at content of tech conferences, I think it is fair to say that AI is getting a lot of attention -- one might even call it an AI hype (like in the 80's). Plenty of ...
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Machine learning and operations research projects
Can someone give me some suggestions for projects that use both machine learning/deep learning and operations research to solve business problems?
Background: I am a student in OR and I am learning ...
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Examples of machine learning applied to operations research?
Can someone give me a few examples, if they exist, of problems in operations research that could be solved using machine learning.
I am aware that machine learning examples are data-driven and do not ...
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What are the tradeoffs between "exact" and Reinforcement Learning methods for solving optimization problems
Exact methods, e.g., models that utilize an MIP approach with a specified objective and constraints, have advantages like the following:
Using off the shelf solvers
Optimality gap provability
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What is the connection of Operations Research and Reinforcement Learning?
I know that Markov Chains and Markov Decision Processes have been studied in the OR community too. But, I was wondering what is the relationship of Operations Research (OR) and Reinforcement Learning (...
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